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A baseline for multi-label image classification using an ensemble of deep convolutional neural networks

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Figure

Fig. 1. An illustration of the employed framework. Varying image scales and data augmentation techniques are used  dur-ing traindur-ing which results in diverse trained models
Table 3. Comparison with state-of-the-art results on three benchmark datasets. (Notations are the same as those in  Ta-ble 2

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